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Affirmative Action in Higher Education in India: Targeting, Catch Up, and Mismatch at IIT-Delhi

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Affirmative Action in Higher Education in India: Targeting, Catch Up, and Mismatch at IIT-Delhi Verónica Frisancho Robles Kala Krishna November 2011 Abstract Affirmative action policies in higher education
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Affirmative Action in Higher Education in India: Targeting, Catch Up, and Mismatch at IIT-Delhi Verónica Frisancho Robles Kala Krishna November 2011 Abstract Affirmative action policies in higher education are used in many countries to try to socially advance historically disadvantaged minorities. Although the underlying social objectives of these policies are rarely criticized, there is intense debate over the actual impact of such preferences in higher education on educational performance and labor outcomes. Most of the work uses U.S. data where clean performance indicators are hard to find. Using a remarkably detailed dataset on the 2008 graduating class from the Indian Institute of Technology (IIT) in Delhi we evaluate the impact of affirmative action policies in higher education on minority students focusing on three central issues in the current debate: targeting, catch up, and mismatch. In addition, we present preliminary evidence on labor market discrimination. We find that admission preferences effectively target minority students who are poorer than the average displaced non-minority student. Moreover, by analyzing the college performance of minority and non-minority students as they progress through college, we find that scheduled caste and scheduled tribe students, especially those in more selective majors, fall behind their same-major peers which is the opposite of catching up. We also identify evidence in favor of the mismatch hypothesis: once we control for selection into majors, minority students who enroll in more selective majors as a consequence of admission preferences end up earning less than their same-caste counterparts in less selective majors. Finally, although there is no evidence of discrimination against minority students in terms of wages, we find that scheduled caste and scheduled tribe students are more likely to get worse jobs, even after controlling for selection. This project was started with the late Professor Sanghamitra Das. We would also like to acknowledge Dr. Vibha Arora who was also involved in the exit survey but not in the rest of the data or its compilation or analysis, and Dr. Sunil Kale for his help in obtaining the data. The Pennsylvania State University. The Pennsylvania State University and NBER. 1 1 Introduction Affirmative action (AA) policies in higher education are used in many countries to try to socially advance historically disadvantaged groups. Although the underlying social objectives of these policies are rarely criticized, there is intense debate over the actual impact of minority preferences in higher education on educational performance and labor outcomes. The debate has mainly focused on three issues: targeting, mismatch, and catch up. It is well known that family income is a strong predictor of performance. Thus, there is great concern about the fairness of targeting based on race, ethnicity, or caste rather than on income. If admission preferences only allow richer students within the minority group to traverse the (lower) hurdles required for admission, then they may be displacing poor students from the non-minority or general group. This is also called the creamy layer problem in India. The second issue is catch up. Students admitted to college under preferences often start off far behind those admitted under regular admission criteria. But how does the gap between these two groups change as both progress through college? Do they catch up or fall further behind? If those admitted under preferences can catch up, even part of the way, then the case for preferences is clearly stronger than if they fall further behind. Opponents of AA also claim that the actual gains for the intended beneficiaries of the policy may not exist. In the extreme case, minority students may even be worse off if they are unprepared for the academic environment they obtain access to through the policy. This argument is known as the mismatch hypothesis: students who do not qualify for ordinary admission would do better if they enrolled at schools and/or majors which are more in line with their credentials. If there is severe mismatch, then preferences may even do more harm than good. Most of the studies to date are narrowly focused on the effects of AA on US minorities college performance and labor outcomes. The US, we think, is a poor setting in which to look for such evidence. In most US higher education settings, selection criteria are relatively nebulous. While institutions do want good students, they pay attention to much more than grades or SAT scores in deciding whom to admit. 1 SAT scores, extracurricular activities, essays, 1 This is partly explained by a large number of people having close to or perfect SAT scores. The best schools could easily fill their seat with only such candidates. However, based on the U.S. experience, there is reason to believe that this would result in a worse entering class (Blau et al., 2004 and Bowen and Bok, 1998). 2 alumnities, interviews, theperceived likelihood of thestudent coming 2 anddonations all matter. Moreover, AA policies in the US are themselves relatively nebulous: even in their heyday, they basically consisted of adding some points for race. There were rarely quotas or large and well documented differences in admission standards. 3 Finally, American students have a huge amount of choice over courses while in college. For example, if smart/serious students take harder courses where good grades are more difficult to obtain, while poor students take the gut courses where an A- is ensured with minimal effort, then grades may provide little information on actual academic performance. For all these reasons, the U.S. may not be the best place to evaluate the effects of AA. We argue that other countries, with transparent selection criteria and rigid course structure, provide much more fertile ground for evaluating the effect of AA policies on minority students. The evidence presented here is particularly important due to its focus on India, which provides a better setting than the US. In India, admission criteria are clear: performance in an open admission exam or in the school leaving exam is all that matters. Moreover, admission preferences imposed by AA in India are far greater than the ones given to African American or Hispanic applicants in the US. India has very strict and binding quotas in higher education in favor of scheduled castes (SC) and scheduled tribes (ST). These groups include what were known as the untouchable castes, which used to be relegated to the most menial occupations, as well as tribal populations who were isolated from the mainstream and often treated as badly as the SC. 4 The quotas result in very large differences in admission standards, which provide a nice natural experiment. 5 Thus, it is not likely that the empirical results for the Indian case are confounded by the program being a marginal one. Our focus on India also helps us overcome selection problems present in US college data. Most higher education institutions in India have a very strict curriculum which minimizes the issue of self selection into easier courses. In this 2 Admissions officers are often rewarded on the basis of acceptance rates. 3 The Texas top 10% law, which guaranteed admission to the top 10% of graduates from all Texas high schools to any state or public University may be one of the few exceptions. See the Texas Higher Education Opportunity Project (THEOP) for more on this. The law was loosened in June Lower castes in India represent a greater share in total population than any minority in the US. Even if we only consider the most disadvantaged castes, SC and ST, their 22.5% share surpasses the 13% share of African Americans in the US. 5 In fact, the quotas are so much in favor of these disadvantaged groups that even with huge differences in admissions cutoffs, some elite schools are not able to fill their quotas. 3 setting, grades are a very good indicator of college performance. Using detailed data 6 on the 2008 graduating class from the Indian Institute of Technology (IIT) in Delhi, this article tries to cast some light on the effects of AA on Indian minorities. In particular, we look at income and grade distributions of minority and non-minority students at IIT-Delhi to provide some basic evidence on targeting. We find that SC/ST students are in general poorer than the others at IIT. Using a supplementary data set on all applicants (in 2009) with information on scores, caste and place of residence, we show that AA seems to be effectively targeting minority students who are poorer than the average displaced general (GE) student. By analyzing the college performance of minority and non-minority students as they progress through college, we find no evidence of catch up: SC and ST students, especially those in more selective majors, actually seem to fall behind their same-major peers. We also test the mismatch hypothesis using labor market outcomes and students self reports on emotional and social well-being while at IIT. Without controlling for selection into selective and non-selective majors, it looks like students in more selective majors earn more. Propensity score matching methods that control for selection in observables reduce the estimated effect of major selectivity on wages, though it remains positive and significant for general students. However, if the wage and selection equations are jointly estimated to take into account the role of unobservables, the positive effect among general students goes away, suggesting it was driven by selection. In other words, general students earn more and choose more selective majors because they are better in terms of unobservables. Even more interesting, minority students in more selective majors end up earning significantly less than their same-race counterparts in less selective majors, which supports the mismatch hypothesis. We also identify some evidence in favor of social mismatch: even after controlling for selection, being enrolled in a more selective major increases stress levels and feelings of not belonging among SC/ST students but the effect goes in the other direction among general students. Although there is no evidence that wages are lower for SC/ST students once selection, 6 An exit survey designed by Professor Sanghamitra Das, Professor Kala Krishna, and Professor Vibha Arora was administered to students in the class that graduated in This data was input using funding from the Pennsylvania State University. We are grateful to Dr. Sunil Kale for allowing this data to be collected and input under his supervision. Data on semester by semester grades was provided by the then Dean for Undergraduates at IIT Delhi, Dr. Sunil Kale. All personal identifers are removed from this data. All other data was collected from public sources. 4 grades, and background characteristics are controlled for, we conclude by asking whether labor market discrimination may be operating in more subtle ways. We find preliminary evidence of discrimination against minority students in terms of the types of jobs they are able to find once they graduate from IIT. Controlling for selection into majors and grades, SC/ST students are less likely to get placed in highly rewarded jobs in the areas of finances and management consulting. This could be due to choices made by the students themselves, in which case the implications of this pattern are benign. For example, if SC/ST students are more risk averse, they may choose a job based on their core competence that pays less but that is more secure than one in finance. The rest of the paper proceeds as follows. Section 2 sketches out the major findings in the literature so far. Much of this work is on the U.S. and is plagued by data problems. Section 3 describes the IIT context and the reservation policies mandated by the Indian government in higher education admissions. Section 4 describes the data. Section 5 presents the evidence we have put together on targeting, catch up, mismatch, and discrimination. Section 6 concludes and describes the limitations of the study, as well as directions for future research. 2 The Evidence to Date AA policies are meant to help historically disadvantaged minorities. However, if only richer students within the minority group benefit from AA preferences, displacing poor students from the advantaged group, the fairness of the policy comes under scrutiny. In the US, for example, the use of race as a proxy for income when targeting the poor has been strongly questioned over the last decade. The current debate focuses on a shift from race-based to economic-based affirmative action policies as proposed by Kahlenberg (1996, 2004) among others. Even though it is true that racial diversity has increased in top colleges in the US, income inequality may have increased as competition to get in has, by all accounts, intensified. Carnevale and Rose (2003) find that 74% of the students at the top 146 colleges in the US came from families in the richest economic quarter while only 3% came from the least advantaged quarter. There is also some indication that AA policies that favor African Americans have disproportionately benefited richer minority students. At the 28 selective colleges studied by Bowen and Bok (1998), 86% of 5 African Americans were middle or upper class students. In India, however, work by Bertrand et al. (2010) suggests that affirmative action successfully targets financially disadvantaged students applying to engineering colleges. Even though caste-based targeting did not benefit the poorest SC/ST students, admission was successfully reallocated from richer to poorer households. In particular, upper-caste applicants displaced by AA were richer than the lower-caste applicants taking their place. Besides targeting, two related issues plague the debate on the appropriateness of AA policies: catch up and mismatch. In India, where quotas in some states reach 50% of college admissions, a large burden comes from the expected negative effect on the average quality of students graduating from higher education institutions. If colleges are forced to admit students from scheduled castes until the quota is met, large reductions in students average ability are expected. The magnitude of these reductions will be determined not only by the level of the quota, but by the initial differences in performance between general and minority students (Kochar, 2010). However, if minority students catch up while in college, this particular cost of AA policies can be greatly reduced. Alon and Tienda (2007) evaluate the 10% rule in Texas (where the top 10% of the graduating class in public high schools was ensured admission to UT Austin) and argue that the likelihood of graduation rose after the policy was implemented, and did so significantly for blacks. Their evidence is consistent with previous studies that find that those admitted under the top 10% rule outperform those who were admitted with a lower class rank but higher SAT scores, suggesting that SAT scores are not a good indicator of college performance and that there may well be considerable catch up. However, Alon and Tienda s (2007) results are far from conclusive if those admitted from worse schools were less prepared for college and are likely to choose less challenging majors. If this is the case, graduation rates per se may be less than fully informative. Sander (2004) finds that the average performance gap between blacks and whites at selective law schools is large and, more importantly, tends to get larger as both groups progress through college. He also finds that boosting black applicants into more selective schools lowers their probability of graduation mostly though reduced grades. It is clear that minority students targeted by AA policies have initial academic credentials that are significantly weaker than those of their non-minority peers. If minority students are not 6 able to close the gap, AA policies that allow them into more selective colleges and/or majors may end up hurting them. If minority students attending more selective schools due to AA policies obtain lower grades than the ones they would have obtained in less selective environments, their labor market outcomes could be worsened by admission preferences. Attempts to empirically evaluate the mismatch hypothesis in the US provide mixed evidence. Rothstein and Yoon (2009) and Sander (2004) find evidence of mismatch in law school. Loury and Garman (1993, 1995) find that blacks in the US get considerable earning gains from attending more selective schools but these gains are offset for black students by lower performance both in terms of grades and probability of graduation. Alon and Tienda (2005) assess the effect of college selectivity on the graduation probability. Using both propensity score matching methods as well as bivariate probit models, they reject the mismatch hypothesis suggesting that blacks and Hispanics in the US are able to catch up. We must keep in mind though that US colleges tend to have low performance graduation requirements so that graduation rates may not be a good measure of academic success. Arcidiacono (2005) estimates a structural model that incorporates application decisions, admission decisions, attendance decisions and future earnings. He argues that removing affirmative action reduces the presence of minority students, especially in top schools, but it does not affect income or college attendance by much. Bertrand et al. (2009) is one of the first studies analyzing the mismatch hypothesis in India. They find that the marginal effect of caste-based admission preferences in Indian engineering colleges is positive for minority students: i.e., they do earn more as a result. However, they gain less than what the students they displace lose. Though their data is better than ours as they have information on accepted and rejected students, they have no information on grades, which account for a large part of the differences in earnings. At the very least, their results are unable to distinguish between the pure gains from graduating from more selective institutions and the loss arising from poorer grades in these institutions. 3 IIT Admission Process and Reservation Policies The IITs are engineering and technology-oriented universities of national importance. They were initially created to train scientists and engineers that could contribute to India s industrial 7 development after its independence. Today, there are fifteen IITs in the country which function as autonomous universities with their own curricula, although they are linked to each other through a common central government council in charge of their administration. All institutes offer programs leading to a Bachelor s degree but some IITs also offer Dual Degrees, Integrated Master of Technology, Master of Science and Master of Arts degrees. Admissions to the undergraduate programs and some graduate programs are conducted through the Joint Entrance Examination (JEE). The admission process is very competitive both because of the difficulty of the open competitive exam and the high number of test takers. The undergraduate acceptance rate through the JEE is less than 1 in 60: over 300,000 annual test takers compete for 5500 seats in undergraduate programs. Only 4000 seats are offered by undergraduate programs at IIT and the rest of the seats correspond to other institutions that also use the JEE to evaluate applicants. The JEE tests the candidate s knowledge of 3 subjects: Chemistry, Mathematics and Physics. After the exam is administered, the average of the marks scored by all candidates is computed for each of the three subjects. These averages give the Minimum Qualifying Marks (MQM) for Ranking in each subject. All students above the MQM in each subject are ranked in terms of their aggregate score to construct a common merit list
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